Overview
Jeroen V.K. Rombouts is an econometrician affiliated with HEC Montréal. His research focuses on Bayesian inference for GARCH models, finite mixture models of volatility, and nonparametric methods for financial time series. He collaborated with Luc Bauwens (CORE, UCLouvain) on Bayesian inference for the Mixture-of-Normals GARCH (MN-GARCH) model.
Key Contributions / Features
- Bauwens and Rombouts (2007) — "Bayesian Inference for the Mixed Conditional Heteroskedasticity Model", The Econometrics Journal 10(2): 408–425: Gibbs sampler for MN-GARCH with griddy-Gibbs for non-conjugate GARCH parameters, Dirichlet conjugate for mixing weights, Laplace marginal likelihood for component number selection; S&P 500 application showing near-IGARCH as a misspecification artifact from ignoring mixture structure.
- Bauwens, Laurent, and Rombouts (2006) — "Multivariate GARCH Models: A Survey", Journal of Applied Econometrics 21(1): 79–109. Three-family taxonomy of MGARCH models; parameter counts, invariance properties, QML theory, two-step DCC estimation, variance targeting, diagnostics, and ten open questions.
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